The Emergence of Metacognitive Processes During Teamwork Simulation-based Training

• In Event: Applying Complexity Frameworks to Self-Regulated Learning Research: From Theoretical Models to Practical Tools

Abstract

Significance and Objectives
Teamwork is a critical component that determines success within extreme environments (e.g., emergency care teams, military teams in war zones[7-9]). As such, there is great value in the development of training methods and devices that support the development of individual team members’ teamwork processes, including simulation-based training (SBT). The efficacy of SBT is bolstered by the ability to automatically evaluate team members’ cognitive, affective, metacognitive, and behavioral processes using multimodal trace data, thereby reducing the dependence on manual analyses (e.g., domain expert review10-12]). While the integration of multimodal data and SBT enhances performance evaluation, it also necessitates a complex systems lens to fully conceptualize and optimize the intricate interactions and dependencies inherent in such education environments.

Theoretical Framework and Case Studies
Our work is an extension of the H-ABCM framework[12,13] which assumes that teamwork is comprised a of a series of temporally dynamic affective (e.g., mutual trust, self-efficacy), behavioral (e.g., communication, coordination), cognitive (e.g., team mental models, team learning), and metacognitive (e.g., planning, evaluation, reflection) processes structured hierarchically with emergent metacognitive processes as a result of interactions between processes. In our presentation, we will situate our theoretical model within three unique STE (e.g., computer-based, high-fidelity mannequin and simulation center, and virtual reality) contexts – K-12, medicine, and defense education. Specifically, we examine the measures and emergent metacognitive teamwork processes of learners (1) collaboratively problem-solving as they learn about the spread of infectious diseases; (2) working within emergency pediatric care teams as they train in an STE with high fidelity mannequins; and (3) train for a battle drill within virtual reality as part of a human-AI team.

Implications and Future Directions
An effective implementation of a multimodal data research approach to STEs demands a strong theoretical grounding to make constructive interpretation of the complexities of teamwork and disentangle multimodal data noise and signal. In this presentation, we describe a theoretical expansion of a model of teamwork competency to include a complex systems lens, highlighting the emergence of metacognition (at the individual and group level) while exploring what type of data are best for the collection of these processes’ traces. Metacognitive processes of planning, evaluating, and reflecting are what drive team adaptability to complex and rapidly shifting environments. Their inclusion within models of teamwork is inherently messy due to the entanglement of metacognition and cognition, affect, and behavior. However, we argue that the inclusion and acknowledgement of these processes for application far outweighs the loss of some distinction between process origination at higher levels within the model. While this work is in the initial theoretical development of a conceptual framework, we are also designing a series of empirical studies across multiple contexts that will be used to refine this framework and train various machine learning models of team dynamics and performance. Based on the outcome of these future studies and the implementation of our framework, we will continue the development of our adaptive and intelligent STEs capable of supporting teams during learning, training, and skill development.

Authors

• Megan Wiedbusch, University of Central Florida

• Daryn Dever, University of Florida

• Roger Azevedo, University of Central Florida